Developing AI attacks/defenses
Reinforcement Learning has numerous applications in the real world thanks to its ability to achieve high performance in a range of environments with little manual oversight. Reinforcement Learning can interact with multiple agents in a shared environment called Multi-Agent Reinforcement Learning. Mu...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2022
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| Online Access: | https://hdl.handle.net/10356/162849 |
| _version_ | 1826121228307398656 |
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| author | Pang, Malcolm Qing Han |
| author2 | Jun Zhao |
| author_facet | Jun Zhao Pang, Malcolm Qing Han |
| author_sort | Pang, Malcolm Qing Han |
| collection | NTU |
| description | Reinforcement Learning has numerous applications in the real world thanks to its ability to achieve high performance in a range of environments with little manual oversight. Reinforcement Learning can interact with multiple agents in a shared environment called Multi-Agent Reinforcement Learning. Multi-Agent Reinforcement Learning allows the interaction between agents. However, Multi-Agent Reinforcement Learning becomes problematic in asynchronous environment. Hence, in our work, we considered an environment with users with their user devices (UDs), downloading information data from the base station and uploading information data to the base station asynchronously via wireless communications. We designed an environment with multiple base station, where user devices (UDs) would be able to download and upload information data asynchronously using 2 agents. Our goal for both agent is to allocate system resources to minimize the total time taken for users to download information data from the base stations and to optimize power output for users uploading information data We utilize a deep reinforcement learning approach and evaluate the performance of the algorithms under a certain configuration. |
| first_indexed | 2024-10-01T05:29:15Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/162849 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:29:15Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1628492022-11-11T02:01:20Z Developing AI attacks/defenses Pang, Malcolm Qing Han Jun Zhao School of Computer Science and Engineering junzhao@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Reinforcement Learning has numerous applications in the real world thanks to its ability to achieve high performance in a range of environments with little manual oversight. Reinforcement Learning can interact with multiple agents in a shared environment called Multi-Agent Reinforcement Learning. Multi-Agent Reinforcement Learning allows the interaction between agents. However, Multi-Agent Reinforcement Learning becomes problematic in asynchronous environment. Hence, in our work, we considered an environment with users with their user devices (UDs), downloading information data from the base station and uploading information data to the base station asynchronously via wireless communications. We designed an environment with multiple base station, where user devices (UDs) would be able to download and upload information data asynchronously using 2 agents. Our goal for both agent is to allocate system resources to minimize the total time taken for users to download information data from the base stations and to optimize power output for users uploading information data We utilize a deep reinforcement learning approach and evaluate the performance of the algorithms under a certain configuration. Bachelor of Engineering (Computer Science) 2022-11-11T02:01:20Z 2022-11-11T02:01:20Z 2022 Final Year Project (FYP) Pang, M. Q. H. (2022). Developing AI attacks/defenses. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162849 https://hdl.handle.net/10356/162849 en SCSE21-0840 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence Pang, Malcolm Qing Han Developing AI attacks/defenses |
| title | Developing AI attacks/defenses |
| title_full | Developing AI attacks/defenses |
| title_fullStr | Developing AI attacks/defenses |
| title_full_unstemmed | Developing AI attacks/defenses |
| title_short | Developing AI attacks/defenses |
| title_sort | developing ai attacks defenses |
| topic | Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence |
| url | https://hdl.handle.net/10356/162849 |
| work_keys_str_mv | AT pangmalcolmqinghan developingaiattacksdefenses |